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Found 12 papers, 5 papers with code
MOMENT: A Family of Open Time-series Foundation Models
Pre-training large models on time-series data is challenging due to (1) the absence of a large and cohesive public time-series repository, and (2) diverse time-series characteristics which make multi-dataset training onerous.
AQuA: A Benchmarking Tool for Label Quality Assessment
We hope that our proposed design space and benchmark enable practitioners to choose the right tools to improve their label quality and that our benchmark enables objective and rigorous evaluation of machine learning tools facing mislabeled data.
Unsupervised Model Selection for Time-series Anomaly Detection
The practical problem of selecting the most accurate model for a given dataset without labels has received little attention in the literature.
Classifying Unstructured Clinical Notes via Automatic Weak Supervision
Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes.
Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact
Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.
Counterfactual Phenotyping with Censored Time-to-Events
Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring.
Weak Supervision for Affordable Modeling of Electrocardiogram Data
We explore the use of multiple weak supervision sources to learn diagnostic models of abnormal heartbeats via human designed heuristics, without using ground truth labels on individual data points.
Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection
In this work, we propose GLUE (Graph Deviation Network with Local Uncertainty Estimation), building on the recently proposed Graph Deviation Network (GDN).
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS
In domains such as clinical research, where data collection and its careful characterization is particularly expensive and tedious, this reliance on pointillisticaly labeled data is one of the biggest roadblocks to the adoption of modern data-hungry ML algorithms.
The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming
Most advanced supervised Machine Learning (ML) models rely on vast amounts of point-by-point labelled training examples.
Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children
We also found that the dynamics of time series features are rich predictors of listener disengagement and backchanneling.
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
By analyzing the clusters of latent embeddings and visualizing them over time, we hypothesize that the clusters correspond to the physiological response patterns that match physicians' intuition.
